In [2]:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt   # plotting
import seaborn as sns   # plotting heatmap

%matplotlib inline
In [26]:
df = pd.read_csv('D:\Github\django-shop\handpose-mouse-simulation\postion_data\Chinese_seven.csv',
                )
df2 = pd.read_csv('D:\Github\django-shop\handpose-mouse-simulation\\target_data\Chinese_seven.csv',
                )
In [13]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2609 entries, 0 to 2608
Data columns (total 63 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   x0      2609 non-null   float64
 1   y0      2609 non-null   float64
 2   z0      2609 non-null   float64
 3   x1      2609 non-null   float64
 4   y1      2609 non-null   float64
 5   z1      2609 non-null   float64
 6   x2      2609 non-null   float64
 7   y2      2609 non-null   float64
 8   z2      2609 non-null   float64
 9   x3      2609 non-null   float64
 10  y3      2609 non-null   float64
 11  z3      2609 non-null   float64
 12  x4      2609 non-null   float64
 13  y4      2609 non-null   float64
 14  z4      2609 non-null   float64
 15  x5      2609 non-null   float64
 16  y5      2609 non-null   float64
 17  z5      2609 non-null   float64
 18  x6      2609 non-null   float64
 19  y6      2609 non-null   float64
 20  z6      2609 non-null   float64
 21  x7      2609 non-null   float64
 22  y7      2609 non-null   float64
 23  z7      2609 non-null   float64
 24  x8      2609 non-null   float64
 25  y8      2609 non-null   float64
 26  z8      2609 non-null   float64
 27  x9      2609 non-null   float64
 28  y9      2609 non-null   float64
 29  z9      2609 non-null   float64
 30  x10     2609 non-null   float64
 31  y10     2609 non-null   float64
 32  z10     2609 non-null   float64
 33  x11     2609 non-null   float64
 34  y11     2609 non-null   float64
 35  z11     2609 non-null   float64
 36  x12     2609 non-null   float64
 37  y12     2609 non-null   float64
 38  z12     2609 non-null   float64
 39  x13     2609 non-null   float64
 40  y13     2609 non-null   float64
 41  z13     2609 non-null   float64
 42  x14     2609 non-null   float64
 43  y14     2609 non-null   float64
 44  z14     2609 non-null   float64
 45  x15     2609 non-null   float64
 46  y15     2609 non-null   float64
 47  z15     2609 non-null   float64
 48  x16     2609 non-null   float64
 49  y16     2609 non-null   float64
 50  z16     2609 non-null   float64
 51  x17     2609 non-null   float64
 52  y17     2609 non-null   float64
 53  z17     2609 non-null   float64
 54  x18     2609 non-null   float64
 55  y18     2609 non-null   float64
 56  z18     2609 non-null   float64
 57  x19     2609 non-null   float64
 58  y19     2609 non-null   float64
 59  z19     2609 non-null   float64
 60  x20     2609 non-null   float64
 61  y20     2609 non-null   float64
 62  z20     2609 non-null   float64
dtypes: float64(63)
memory usage: 1.3 MB
In [17]:
df.head()
Out[17]:
x0 y0 z0 x1 y1 z1 x2 y2 z2 x3 ... z17 x18 y18 z18 x19 y19 z19 x20 y20 z20
0 0.588279 0.687364 -0.000058 0.553355 0.663248 -0.002852 0.536861 0.606557 -0.021549 0.524479 ... -0.071636 0.572576 0.586915 -0.092522 0.568609 0.622625 -0.079594 0.579497 0.640591 -0.065896
1 0.588614 0.689675 -0.000064 0.553241 0.661748 0.001669 0.536016 0.607307 -0.015098 0.523905 ... -0.070888 0.570480 0.587335 -0.089173 0.567368 0.622707 -0.077386 0.578273 0.641378 -0.064499
2 0.586556 0.691868 -0.000073 0.551673 0.661805 0.000363 0.534590 0.606866 -0.017144 0.522370 ... -0.070475 0.569727 0.586521 -0.088980 0.567061 0.622435 -0.076765 0.578557 0.641623 -0.063537
3 0.589569 0.695159 -0.000055 0.553746 0.663853 0.001502 0.536343 0.609131 -0.016101 0.523076 ... -0.069789 0.569331 0.587703 -0.085456 0.567234 0.623264 -0.073273 0.579543 0.640219 -0.060835
4 0.589958 0.698058 -0.000064 0.554912 0.664763 0.004362 0.537255 0.611345 -0.011062 0.524839 ... -0.066170 0.571998 0.589540 -0.079986 0.570188 0.625389 -0.068290 0.582433 0.643111 -0.057089

5 rows × 63 columns

In [19]:
df.describe()
Out[19]:
x0 y0 z0 x1 y1 z1 x2 y2 z2 x3 ... z17 x18 y18 z18 x19 y19 z19 x20 y20 z20
count 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 ... 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000 2609.000000
mean 0.561361 0.610462 -0.000042 0.535539 0.576024 -0.002198 0.520824 0.510569 -0.017301 0.514195 ... -0.060590 0.548124 0.466603 -0.090267 0.545726 0.501308 -0.087598 0.552539 0.523871 -0.080541
std 0.117577 0.155643 0.000034 0.113377 0.148368 0.027114 0.117840 0.146205 0.032388 0.127809 ... 0.056258 0.136597 0.159210 0.052114 0.131283 0.158199 0.055526 0.126676 0.159470 0.062044
min 0.000000 0.000000 -0.000280 0.000000 0.000000 -0.150558 0.000000 0.000000 -0.255492 0.000000 ... -0.270283 0.000000 0.000000 -0.354165 0.000000 0.000000 -0.417686 0.000000 0.000000 -0.464105
25% 0.497981 0.487984 -0.000057 0.473636 0.461210 -0.020075 0.439839 0.390517 -0.034923 0.419352 ... -0.083197 0.466061 0.332624 -0.106323 0.470148 0.382557 -0.100213 0.488226 0.416695 -0.088580
50% 0.570350 0.635907 -0.000037 0.549373 0.591654 -0.003276 0.537857 0.510321 -0.013685 0.531835 ... -0.049543 0.560694 0.466195 -0.077432 0.560572 0.514046 -0.073953 0.570365 0.538854 -0.063731
75% 0.650696 0.709889 -0.000020 0.623481 0.680246 0.010590 0.615680 0.620410 0.000013 0.619309 ... -0.020931 0.653239 0.590856 -0.061346 0.646001 0.620967 -0.058130 0.648004 0.641623 -0.048262
max 0.757160 0.931839 0.000045 0.721734 0.908269 0.097229 0.720461 0.852178 0.094714 0.734952 ... 0.077458 0.784546 0.819508 0.020081 0.762023 0.857951 0.008688 0.751914 0.882951 0.015957

8 rows × 63 columns

In [23]:
## Plot the time series
plt.style.use('fivethirtyeight')
df.plot(subplots=True,
        layout=(63, 3),
        figsize=(22,22),
        fontsize=10, 
        linewidth=2,
        sharex=False,
        title='Visualization of the original Time Series')
plt.show()

C:\Users\Qiao\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\plotting\_matplotlib\tools.py:400: MatplotlibDeprecationWarning: 
The is_first_col function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use ax.get_subplotspec().is_first_col() instead.
  if ax.is_first_col():
C:\Users\Qiao\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\plotting\_matplotlib\tools.py:400: MatplotlibDeprecationWarning: 
The is_first_col function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use ax.get_subplotspec().is_first_col() instead.
  if ax.is_first_col():
2021-06-23T05:18:38.193321 image/svg+xml Matplotlib v3.4.1, https://matplotlib.org/
In [22]:
#heatmap visualization of the correlation matrix
corr_matrix = df.corr(method='spearman')
f, ax = plt.subplots(figsize=(16,8))
sns.heatmap(corr_matrix, annot=True, fmt='.2f', linewidth=0.4,
            annot_kws={"size": 10}, cmap='coolwarm', ax=ax)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
plt.show()
2021-06-23T05:14:42.486972 image/svg+xml Matplotlib v3.4.1, https://matplotlib.org/
In [27]:
df_transform = df2.copy().pct_change(1)
df_transform.head()
Out[27]:
0.6108246445655823 0.7414093613624573 -1.7450931409257464e-05 0.5736578106880188 0.7333539128303528 -0.026967162266373634 0.5405039191246033 0.6930084228515625 -0.04962950199842453 0.5292852520942688 ... -0.059472326189279556 0.6099294424057007 0.6133171916007996 -0.09297827631235123 0.5996366143226624 0.648635983467102 -0.08817566931247711 0.5965439677238464 0.6814737915992737 -0.07547677308320999
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 -0.007395 -0.021145 -6.114154 -0.017592 -0.041910 -0.171146 -0.016039 -0.051162 -0.081320 -0.017058 ... 0.034428 -0.000296 -0.053663 0.023380 -0.008027 -0.060220 0.098981 -0.016668 -0.057392 0.173286
2 -0.006606 -0.020439 -0.263001 -0.007250 -0.022818 -0.093176 -0.007399 -0.029314 -0.046301 -0.006417 ... -0.004210 -0.008093 -0.033725 0.016241 -0.006286 -0.028200 -0.003716 -0.005927 -0.025903 -0.037947
3 -0.001761 -0.023737 0.864003 -0.004737 -0.021633 0.522108 -0.008633 -0.024532 0.335937 -0.005547 ... 0.168904 -0.010165 -0.024993 0.127894 -0.005803 -0.014558 0.065773 -0.001526 -0.015080 0.009086
4 -0.005433 -0.020225 -0.570420 -0.001490 -0.021076 -0.275870 0.002710 -0.021583 -0.259765 -0.001647 ... -0.311983 -0.004046 -0.016519 -0.242291 -0.004097 -0.014715 -0.214409 -0.003898 -0.018799 -0.214688

5 rows × 63 columns

In [28]:
plt.style.use('fivethirtyeight')
df_transform.plot(subplots=True,
                  layout=(63, 3),
                  figsize=(24,24),
                  fontsize=10, 
                  linewidth=2, 
                  title='Visualization of the transformed Features')
plt.show()

C:\Users\Qiao\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\plotting\_matplotlib\tools.py:400: MatplotlibDeprecationWarning: 
The is_first_col function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use ax.get_subplotspec().is_first_col() instead.
  if ax.is_first_col():
C:\Users\Qiao\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\plotting\_matplotlib\tools.py:400: MatplotlibDeprecationWarning: 
The is_first_col function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use ax.get_subplotspec().is_first_col() instead.
  if ax.is_first_col():
2021-06-23T05:27:30.621373 image/svg+xml Matplotlib v3.4.1, https://matplotlib.org/
In [ ]: